Artificial intelligence is evolving beyond automation and prediction into systems that can interpret human communication and generate meaningful insights. One of the most impactful areas of this shift is the ability to transform conversational data into signals that support awareness, accessibility, and informed decision-making. In this session, Aoi Minamoto (Sophie) presents a practical approach to building human-centered AI systems that convert natural conversations into measurable cognitive insights. Drawing from her work with KinaBot and real-world deployments, she demonstrates how conversational data can be structured, analyzed, and translated into outputs that are both usable and relevant. Participants will explore how AI systems can move from raw voice or text inputs to actionable insights, while maintaining clarity, usability, and trust. The session also highlights the importance of designing AI systems that prioritize human understanding rather than complexity, ensuring that outputs are meaningful and applicable in real-world contexts.
Through examples across healthcare and industrial environments, attendees will gain a grounded understanding of how these systems are designed, deployed, and scaled. This session connects technical capability with practical application, showing how Business Analysts and technology professionals can contribute to AI solutions that focus on real human value.\
Learning Objectives
By the end of this session, participants will be able to:
Understand how conversational data can be transformed into meaningful insights
Recognize the principles of human-centered AI design
Identify how AI systems generate cognitive signals from natural communication
Understand practical applications of conversational AI in real-world environments
Explore how Business Analysts can contribute to AI systems focused on human impact
Course Outline
Introduction to Human-Centered AI
Understanding the shift toward AI systems designed for human awareness and usability.
From Conversation to Data
How voice and text inputs are captured and structured for analysis.
Generating Meaningful Insights
How AI models interpret conversational patterns to produce cognitive signals.
Real-World Applications
Examples from healthcare, industrial systems, and everyday awareness tools.
Designing for Trust and Accessibility
Ensuring AI outputs are understandable, usable, and aligned with human needs.
Audience
This session is ideal for:
Business Analysts interested in AI and human-centered system design
Product and technology professionals working with conversational data and AI
Professionals in healthcare, innovation, and digital transformation
Anyone interested in practical applications of AI that focus on human understanding
Instructor Profile
Aoi Minamoto (Sophie) is an AI entrepreneur and engineer focused on human-centered artificial intelligence and cognitive insight technologies. She is the founder of AImoji LLC and the creator of KinaBot, an AI system that analyzes conversational data to generate cognitive trend insights for everyday awareness.
Her work bridges applied AI, data science, and real-world deployment across both industrial and healthcare-related contexts. In addition to her startup work, Aoi is an engineer at Toyota Battery Manufacturing North Carolina, where she works on machine and data systems.
Aoi is also an active contributor to the research community, serving as a reviewer for ACM conferences and an invited judge for international innovation and business awards. Her work has been featured on global platforms and innovation programs, reflecting her commitment to building AI systems that are practical, accessible, and impactful.